Data Science in Food Industry
What Data Science is?
Data Science is a combination of various tools, algorithms, and machine learning principles to extract knowledge and insights from structured and unstructured data.
What is the food Industry?
The term food industry covers a series of activities like processing, conversion, preparation, preservation, and packaging of foodstuffs.
The food industry is the most dynamic today. Products need to be continuously evolving. The food industry involves producers, distributors, restaurants, etc. Food manufacturing companies are actively getting deeper into data science to improve inventory management, Meeting quality standards, to meet customer demands.
Role of Data Science in the Food Industry?
1. Predicting Shelf Life — data science and analytics help in predicting the shelf life of the food products like bakery products, wine, etc by using predictive analysis. This may help them to save food products before it gets wasted.
2. Sentiment Analysis — Sentiment analysis is analyzing the customer’s inclination, emotions, and feelings towards a product, its brand, and personal reviews regarding that. In Sentiment, analysis data is gathered from different social media platforms. This data is interpreted, analyzed, and visualized to get insights out of it. This may help industries to know “What Customer Wants?”
This helps in understanding the trends to make better business decisions.
Using techniques like natural language processing, data analysis tools go through the text and categorize it into positive, negative, or neutral. Any negative review can be analyzed at scale and preventive actions can be taken.
3 Marketing — For any business, it is very much important to spread awareness and acquire potential customers. Data Science and analytics help the food industry in identifying potential customers.
Data can be analyzed to understand the demographics and needs to run highly targeted marketing campaigns.
For example: when we use applications to order food like McDonald’s app, we not only order food and pay for it. We also get offers, complimentary items, and deals. This will help the company to gather the data of the customer. They can see what products you like, how often you order, which location you visit, and so on. This will help them to provide a personalized and relevant experience.
4. On-time deliveries — Big data and analytics helps in understanding factors like traffic, climatic conditions, shortest route, distance, etc. This information is used to make a model for the estimation of time required to deliver the food product from source to destination.
Examples of Using Big Data Analytics and Predictive Data in the Food Industry
1. Connecterra — It has developed “Ida,” an AI-powered program that can help farmers predict certain health issues with their cattle. Ida is based on a sensor device that fitted to cows on a collar and app for farmers. Using data analytics and machine learning, Ida can turn animal behavior data into predictive analytics and actionable recommendations instead of just giving farmers data points. It helps the individual farmer to improve productivity and manage the herd.
2. The yield — It is an Australian-based startup that focuses on the agriculture industry. It has products that use a variety of sensor technology to monitor agriculture. Data provided by the sensors are used for forecasting and predictions.
3. Bright seed: It is an American company that uses AI, predictive analysis, big data to identify beneficial plant compounds. Data is used to create bio-actives that can be added to food to make it healthy.
4. Quantzig — It focuses on the business end of the food industry. Its products help companies to make better decisions related to marketing, sales, and pricing.
The application of Data Science, Artificial intelligence, big data, analytics plays a vital role in the production of the right products, sourcing, quality of food products, delivery of food to the doorstep of customers.
The importance of data cannot be ignored to make better business decisions.
